pyriemann.transfer.TLClassifier

class pyriemann.transfer.TLClassifier(target_domain, estimator, domain_weight=None)

Transfer learning wrapper for classifiers.

This is a wrapper for any classifier that converts extended labels used in transfer learning into the usual y array to train a classifier of choice.

Parameters:
target_domainstr

Domain to consider as target.

estimatorBaseClassifier

The classifier to apply on matrices.

domain_weightNone | dict, default=None

Weights to combine data from each domain to train the classifier. The dict contains key=domain_name and value=weight_to_assign. If None, it uses equal weights.

See also

TLRegressor

Notes

Added in version 0.4.

__init__(target_domain, estimator, domain_weight=None)

Init.

fit(X, y_enc)

Fit TLClassifier.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels) or shape (n_vectors, n_ts)

Set of SPD matrices or tangent vectors.

y_encndarray, shape (n_matrices,) or shape (n_vectors,)

Extended labels for each matrix or vector.

Returns:
selfTLClassifier instance

The TLClassifier instance.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

predict(X)

Get the predictions.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels) or shape (n_vectors, n_ts)

Set of SPD matrices or tangent vectors.

Returns:
predndarray, shape (n_matrices,) or shape (n_vectors,)

Predictions according to the estimator.

predict_proba(X)

Get the probability.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels) or shape (n_vectors, n_ts)

Set of SPD matrices or tangent vectors.

Returns:
predndarray, shape (n_matrices, n_classes) or shape (n_vectors, n_classes)

Predictions for each matrix or vector.

score(X, y_enc)

Return the mean accuracy on the given test data and labels.

Parameters:
Xndarray, shape (n_matrices, n_channels, n_channels) or shape (n_vectors, n_ts)

Set of SPD matrices or tangent vectors.

y_encndarray, shape (n_matrices,) or shape (n_vectors,)

Extended labels for each matrix or vector.

Returns:
scorefloat

Mean accuracy of self.predict(X) wrt. y.

set_fit_request(*, y_enc: bool | None | str = '$UNCHANGED$') → TLClassifier

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
y_encstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for y_enc parameter in fit.

Returns:
selfobject

The updated object.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

set_score_request(*, y_enc: bool | None | str = '$UNCHANGED$') → TLClassifier

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
y_encstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for y_enc parameter in score.

Returns:
selfobject

The updated object.

Examples using pyriemann.transfer.TLClassifier

Motor imagery classification by transfer learning

Motor imagery classification by transfer learning

Comparison of pipelines for transfer learning

Comparison of pipelines for transfer learning